README.md
In atusy/pingAnalysis: Analyze Ping Log Files
pingAnalysis
Analyze ping log.
Installation
Requires R >= 4.1.
You can install the development version from
GitHub with:
# install.packages("devtools")
devtools::install_github("atusy/pingAnalysis")
Examples
- Load the package with:
library(pingAnalysis)
- Prepare CSV file with three columns without header:
- timestamp with YMDhms-style
- IPv4 address with prefix-size
- Ping timing.
- indicates timeout.
20200101000001,192.168.1.1/24,1
20200101000002,192.168.1.1/24,-
20200101000003,192.168.1.2/24,1
20200101000004,192.168.1.1/24,-
20200101000005,192.168.1.2/24,-
1. Treat all ping timeout as server being out of service.
Prepare data
log_csv <- tempfile(fileext = ".csv")
log_df <- tibble::tibble(
timestamp = 20200101000000 + seq(5),
address = "192.168.1.1/24",
ping = c("1", "-", "1", "-", "-")
)
readr::write_csv(log_df, log_csv, col_names = FALSE)
View data
20200101000001,192.168.1.1/24,1
20200101000002,192.168.1.1/24,-
20200101000003,192.168.1.1/24,1
20200101000004,192.168.1.1/24,-
20200101000005,192.168.1.1/24,-
Analyze
measure_all_timeout(read_log(log_csv))
#> # A tibble: 2 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:02 2020-01-01 00:00:03 1
#> 2 192.168.1.1/24 2020-01-01 00:00:04 NA 2
2. Treat ping timeout as server being out of service if timeout continues N-times or more.
Data inherits from the previous example.
Analyze
N = 1L is equivalent to measure_all_timeout()
measure_timeout(read_log(log_csv), N = 1L)
#> # A tibble: 2 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:02 2020-01-01 00:00:03 1
#> 2 192.168.1.1/24 2020-01-01 00:00:04 NA 2
N = 2L
measure_timeout(read_log(log_csv), N = 2L)
#> # A tibble: 1 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:04 NA 2
3. Treat ping result as server being overloaded if rolling mean of ping is greater than or equal to threshold.
Prepare data
basetime <- 20200101000000
pings <- c(
0, 11, 0, 11, 12, 0, 11, 12, 13,
0, 11, 0, 11, 12, 0, 11, NA, 13
) |> as.character() |> dplyr::na_if("0") |> dplyr::coalesce("-")
N <- length(pings) / 2L
log_df <- tibble::tibble(
timestamp = rep(basetime + seq(N), 2L),
address = paste0("192.168.1.", 1L:2L, "/24") |> rep(each = N),
ping = pings
)
readr::write_csv(log_df, log_csv, col_names = FALSE)
View data
20200101000001,192.168.1.1/24,-
20200101000002,192.168.1.1/24,11
20200101000003,192.168.1.1/24,-
20200101000004,192.168.1.1/24,11
20200101000005,192.168.1.1/24,12
20200101000006,192.168.1.1/24,-
20200101000007,192.168.1.1/24,11
20200101000008,192.168.1.1/24,12
20200101000009,192.168.1.1/24,13
20200101000001,192.168.1.2/24,-
20200101000002,192.168.1.2/24,11
20200101000003,192.168.1.2/24,-
20200101000004,192.168.1.2/24,11
20200101000005,192.168.1.2/24,12
20200101000006,192.168.1.2/24,-
20200101000007,192.168.1.2/24,11
20200101000008,192.168.1.2/24,-
20200101000009,192.168.1.2/24,13
Analyze
Read log
log_df <- read_log(log_csv)
Analyze varying parameters:
- m: window size for rolling mean
- t: threshold of ping
- N: counts of continuous errors
measure_overload(log_df, m = 1L, t = 10, N = 1L)
#> # A tibble: 8 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:02 2020-01-01 00:00:03 1
#> 2 192.168.1.2/24 2020-01-01 00:00:02 NA 1
#> 3 192.168.1.1/24 2020-01-01 00:00:04 2020-01-01 00:00:06 2
#> 4 192.168.1.2/24 2020-01-01 00:00:04 NA 2
#> 5 192.168.1.2/24 2020-01-01 00:00:07 2020-01-01 00:00:08 1
#> 6 192.168.1.1/24 2020-01-01 00:00:07 NA 2
#> 7 192.168.1.1/24 2020-01-01 00:00:09 NA 1
#> 8 192.168.1.2/24 2020-01-01 00:00:09 NA 1
measure_overload(log_df, m = 2L, t = 10, N = 1L)
#> # A tibble: 3 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:05 2020-01-01 00:00:06 1
#> 2 192.168.1.2/24 2020-01-01 00:00:05 NA 1
#> 3 192.168.1.1/24 2020-01-01 00:00:08 NA 2
measure_overload(log_df, m = 1L, t = 10, N = 2L)
#> # A tibble: 3 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:04 2020-01-01 00:00:06 2
#> 2 192.168.1.2/24 2020-01-01 00:00:04 NA 2
#> 3 192.168.1.1/24 2020-01-01 00:00:07 NA 2
4. Treat ping result as subnet, or network-switch, being out-of-order if all servers fail to response for N-times or more.
Prepare data
basetime <- 20200101000000
pings <- c(
# 1 2 3 4 5 6 7 8 9 10
1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 0, 1, 1, 0, 1,
1, 1, 1, 0, 0, 0, 0, 1, 0, 1
)
N <- length(pings) / 3L
log_df <- tibble::tibble(
timestamp = rep(basetime + seq(N), 3L),
address = paste0("192.168.1.", 1L:3L, "/24") |> rep(each = N),
ping = ifelse(pings == 0, "-", as.character(pings))
)
log_df |>
dplyr::mutate(
address = stringr::str_replace(address, "^192\\.168\\.1\\.", "192.168.2.")
) |>
dplyr::bind_rows(log_df) |>
readr::write_csv(log_csv, col_names = FALSE)
View data
20200101000001,192.168.2.1/24,1
20200101000002,192.168.2.1/24,-
20200101000003,192.168.2.1/24,-
20200101000004,192.168.2.1/24,-
20200101000005,192.168.2.1/24,-
20200101000006,192.168.2.1/24,-
20200101000007,192.168.2.1/24,-
20200101000008,192.168.2.1/24,-
20200101000009,192.168.2.1/24,-
20200101000010,192.168.2.1/24,1
20200101000001,192.168.2.2/24,1
20200101000002,192.168.2.2/24,1
20200101000003,192.168.2.2/24,-
20200101000004,192.168.2.2/24,-
20200101000005,192.168.2.2/24,-
20200101000006,192.168.2.2/24,-
20200101000007,192.168.2.2/24,1
20200101000008,192.168.2.2/24,1
20200101000009,192.168.2.2/24,-
20200101000010,192.168.2.2/24,1
20200101000001,192.168.2.3/24,1
20200101000002,192.168.2.3/24,1
20200101000003,192.168.2.3/24,1
20200101000004,192.168.2.3/24,-
20200101000005,192.168.2.3/24,-
20200101000006,192.168.2.3/24,-
20200101000007,192.168.2.3/24,-
20200101000008,192.168.2.3/24,1
20200101000009,192.168.2.3/24,-
20200101000010,192.168.2.3/24,1
20200101000001,192.168.1.1/24,1
20200101000002,192.168.1.1/24,-
20200101000003,192.168.1.1/24,-
20200101000004,192.168.1.1/24,-
20200101000005,192.168.1.1/24,-
20200101000006,192.168.1.1/24,-
20200101000007,192.168.1.1/24,-
20200101000008,192.168.1.1/24,-
20200101000009,192.168.1.1/24,-
20200101000010,192.168.1.1/24,1
20200101000001,192.168.1.2/24,1
20200101000002,192.168.1.2/24,1
20200101000003,192.168.1.2/24,-
20200101000004,192.168.1.2/24,-
20200101000005,192.168.1.2/24,-
20200101000006,192.168.1.2/24,-
20200101000007,192.168.1.2/24,1
20200101000008,192.168.1.2/24,1
20200101000009,192.168.1.2/24,-
20200101000010,192.168.1.2/24,1
20200101000001,192.168.1.3/24,1
20200101000002,192.168.1.3/24,1
20200101000003,192.168.1.3/24,1
20200101000004,192.168.1.3/24,-
20200101000005,192.168.1.3/24,-
20200101000006,192.168.1.3/24,-
20200101000007,192.168.1.3/24,-
20200101000008,192.168.1.3/24,1
20200101000009,192.168.1.3/24,-
20200101000010,192.168.1.3/24,1
Analyze
Read log.
log_df <- read_log(log_csv)
Assume log contains all the required IP addresses.
address_all <- unique(log_df$address)
address_all
#> [1] "192.168.1.1/24" "192.168.1.2/24" "192.168.1.3/24" "192.168.2.1/24"
#> [5] "192.168.2.2/24" "192.168.2.3/24"
Do the analysis varying N.
measure_subnet_timeout(log_df, N = 1L, address_all = address_all)
#> # A tibble: 4 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.0/24 2020-01-01 00:00:04 2020-01-01 00:00:07 3
#> 2 192.168.2.0/24 2020-01-01 00:00:04 2020-01-01 00:00:07 3
#> 3 192.168.1.0/24 2020-01-01 00:00:09 2020-01-01 00:00:10 1
#> 4 192.168.2.0/24 2020-01-01 00:00:09 2020-01-01 00:00:10 1
measure_subnet_timeout(log_df, N = 2L, address_all = address_all)
#> # A tibble: 2 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.0/24 2020-01-01 00:00:04 2020-01-01 00:00:07 3
#> 2 192.168.2.0/24 2020-01-01 00:00:04 2020-01-01 00:00:07 3
atusy/pingAnalysis documentation built on Jan. 3, 2022, 12:47 p.m.
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pingAnalysis
Analyze ping log.
Installation
Requires R >= 4.1.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("atusy/pingAnalysis")
Examples
- Load the package with:
library(pingAnalysis)
- Prepare CSV file with three columns without header:
- timestamp with YMDhms-style
- IPv4 address with prefix-size
- Ping timing.
-indicates timeout.
20200101000001,192.168.1.1/24,1
20200101000002,192.168.1.1/24,-
20200101000003,192.168.1.2/24,1
20200101000004,192.168.1.1/24,-
20200101000005,192.168.1.2/24,-
1. Treat all ping timeout as server being out of service.
Prepare data
log_csv <- tempfile(fileext = ".csv")
log_df <- tibble::tibble(
timestamp = 20200101000000 + seq(5),
address = "192.168.1.1/24",
ping = c("1", "-", "1", "-", "-")
)
readr::write_csv(log_df, log_csv, col_names = FALSE)
20200101000001,192.168.1.1/24,1
20200101000002,192.168.1.1/24,-
20200101000003,192.168.1.1/24,1
20200101000004,192.168.1.1/24,-
20200101000005,192.168.1.1/24,-
Analyze
measure_all_timeout(read_log(log_csv))
#> # A tibble: 2 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:02 2020-01-01 00:00:03 1
#> 2 192.168.1.1/24 2020-01-01 00:00:04 NA 2
2. Treat ping timeout as server being out of service if timeout continues N-times or more.
Data inherits from the previous example.
Analyze
N = 1Lis equivalent tomeasure_all_timeout()
measure_timeout(read_log(log_csv), N = 1L)
#> # A tibble: 2 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:02 2020-01-01 00:00:03 1
#> 2 192.168.1.1/24 2020-01-01 00:00:04 NA 2
N = 2L
measure_timeout(read_log(log_csv), N = 2L)
#> # A tibble: 1 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:04 NA 2
3. Treat ping result as server being overloaded if rolling mean of ping is greater than or equal to threshold.
Prepare data
basetime <- 20200101000000
pings <- c(
0, 11, 0, 11, 12, 0, 11, 12, 13,
0, 11, 0, 11, 12, 0, 11, NA, 13
) |> as.character() |> dplyr::na_if("0") |> dplyr::coalesce("-")
N <- length(pings) / 2L
log_df <- tibble::tibble(
timestamp = rep(basetime + seq(N), 2L),
address = paste0("192.168.1.", 1L:2L, "/24") |> rep(each = N),
ping = pings
)
readr::write_csv(log_df, log_csv, col_names = FALSE)
20200101000001,192.168.1.1/24,-
20200101000002,192.168.1.1/24,11
20200101000003,192.168.1.1/24,-
20200101000004,192.168.1.1/24,11
20200101000005,192.168.1.1/24,12
20200101000006,192.168.1.1/24,-
20200101000007,192.168.1.1/24,11
20200101000008,192.168.1.1/24,12
20200101000009,192.168.1.1/24,13
20200101000001,192.168.1.2/24,-
20200101000002,192.168.1.2/24,11
20200101000003,192.168.1.2/24,-
20200101000004,192.168.1.2/24,11
20200101000005,192.168.1.2/24,12
20200101000006,192.168.1.2/24,-
20200101000007,192.168.1.2/24,11
20200101000008,192.168.1.2/24,-
20200101000009,192.168.1.2/24,13
Analyze
Read log
log_df <- read_log(log_csv)
Analyze varying parameters:
- m: window size for rolling mean
- t: threshold of ping
- N: counts of continuous errors
measure_overload(log_df, m = 1L, t = 10, N = 1L)
#> # A tibble: 8 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:02 2020-01-01 00:00:03 1
#> 2 192.168.1.2/24 2020-01-01 00:00:02 NA 1
#> 3 192.168.1.1/24 2020-01-01 00:00:04 2020-01-01 00:00:06 2
#> 4 192.168.1.2/24 2020-01-01 00:00:04 NA 2
#> 5 192.168.1.2/24 2020-01-01 00:00:07 2020-01-01 00:00:08 1
#> 6 192.168.1.1/24 2020-01-01 00:00:07 NA 2
#> 7 192.168.1.1/24 2020-01-01 00:00:09 NA 1
#> 8 192.168.1.2/24 2020-01-01 00:00:09 NA 1
measure_overload(log_df, m = 2L, t = 10, N = 1L)
#> # A tibble: 3 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:05 2020-01-01 00:00:06 1
#> 2 192.168.1.2/24 2020-01-01 00:00:05 NA 1
#> 3 192.168.1.1/24 2020-01-01 00:00:08 NA 2
measure_overload(log_df, m = 1L, t = 10, N = 2L)
#> # A tibble: 3 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.1/24 2020-01-01 00:00:04 2020-01-01 00:00:06 2
#> 2 192.168.1.2/24 2020-01-01 00:00:04 NA 2
#> 3 192.168.1.1/24 2020-01-01 00:00:07 NA 2
4. Treat ping result as subnet, or network-switch, being out-of-order if all servers fail to response for N-times or more.
Prepare data
basetime <- 20200101000000
pings <- c(
# 1 2 3 4 5 6 7 8 9 10
1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 0, 0, 0, 1, 1, 0, 1,
1, 1, 1, 0, 0, 0, 0, 1, 0, 1
)
N <- length(pings) / 3L
log_df <- tibble::tibble(
timestamp = rep(basetime + seq(N), 3L),
address = paste0("192.168.1.", 1L:3L, "/24") |> rep(each = N),
ping = ifelse(pings == 0, "-", as.character(pings))
)
log_df |>
dplyr::mutate(
address = stringr::str_replace(address, "^192\\.168\\.1\\.", "192.168.2.")
) |>
dplyr::bind_rows(log_df) |>
readr::write_csv(log_csv, col_names = FALSE)
20200101000001,192.168.2.1/24,1
20200101000002,192.168.2.1/24,-
20200101000003,192.168.2.1/24,-
20200101000004,192.168.2.1/24,-
20200101000005,192.168.2.1/24,-
20200101000006,192.168.2.1/24,-
20200101000007,192.168.2.1/24,-
20200101000008,192.168.2.1/24,-
20200101000009,192.168.2.1/24,-
20200101000010,192.168.2.1/24,1
20200101000001,192.168.2.2/24,1
20200101000002,192.168.2.2/24,1
20200101000003,192.168.2.2/24,-
20200101000004,192.168.2.2/24,-
20200101000005,192.168.2.2/24,-
20200101000006,192.168.2.2/24,-
20200101000007,192.168.2.2/24,1
20200101000008,192.168.2.2/24,1
20200101000009,192.168.2.2/24,-
20200101000010,192.168.2.2/24,1
20200101000001,192.168.2.3/24,1
20200101000002,192.168.2.3/24,1
20200101000003,192.168.2.3/24,1
20200101000004,192.168.2.3/24,-
20200101000005,192.168.2.3/24,-
20200101000006,192.168.2.3/24,-
20200101000007,192.168.2.3/24,-
20200101000008,192.168.2.3/24,1
20200101000009,192.168.2.3/24,-
20200101000010,192.168.2.3/24,1
20200101000001,192.168.1.1/24,1
20200101000002,192.168.1.1/24,-
20200101000003,192.168.1.1/24,-
20200101000004,192.168.1.1/24,-
20200101000005,192.168.1.1/24,-
20200101000006,192.168.1.1/24,-
20200101000007,192.168.1.1/24,-
20200101000008,192.168.1.1/24,-
20200101000009,192.168.1.1/24,-
20200101000010,192.168.1.1/24,1
20200101000001,192.168.1.2/24,1
20200101000002,192.168.1.2/24,1
20200101000003,192.168.1.2/24,-
20200101000004,192.168.1.2/24,-
20200101000005,192.168.1.2/24,-
20200101000006,192.168.1.2/24,-
20200101000007,192.168.1.2/24,1
20200101000008,192.168.1.2/24,1
20200101000009,192.168.1.2/24,-
20200101000010,192.168.1.2/24,1
20200101000001,192.168.1.3/24,1
20200101000002,192.168.1.3/24,1
20200101000003,192.168.1.3/24,1
20200101000004,192.168.1.3/24,-
20200101000005,192.168.1.3/24,-
20200101000006,192.168.1.3/24,-
20200101000007,192.168.1.3/24,-
20200101000008,192.168.1.3/24,1
20200101000009,192.168.1.3/24,-
20200101000010,192.168.1.3/24,1
Analyze
Read log.
log_df <- read_log(log_csv)
Assume log contains all the required IP addresses.
address_all <- unique(log_df$address)
address_all
#> [1] "192.168.1.1/24" "192.168.1.2/24" "192.168.1.3/24" "192.168.2.1/24"
#> [5] "192.168.2.2/24" "192.168.2.3/24"
Do the analysis varying N.
measure_subnet_timeout(log_df, N = 1L, address_all = address_all)
#> # A tibble: 4 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.0/24 2020-01-01 00:00:04 2020-01-01 00:00:07 3
#> 2 192.168.2.0/24 2020-01-01 00:00:04 2020-01-01 00:00:07 3
#> 3 192.168.1.0/24 2020-01-01 00:00:09 2020-01-01 00:00:10 1
#> 4 192.168.2.0/24 2020-01-01 00:00:09 2020-01-01 00:00:10 1
measure_subnet_timeout(log_df, N = 2L, address_all = address_all)
#> # A tibble: 2 × 4
#> address start end n_timeout
#> <chr> <dttm> <dttm> <int>
#> 1 192.168.1.0/24 2020-01-01 00:00:04 2020-01-01 00:00:07 3
#> 2 192.168.2.0/24 2020-01-01 00:00:04 2020-01-01 00:00:07 3
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